To overcome the 180° coverage limitation of conventional reflective Reconfigurable Intelligent Surfaces (RIS) in challenging Non-Line-of-Sight (NLoS) environments like underground coal mines, this paper proposes the deployment of a Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The STAR-RIS achieves full 360° signal coverage, effectively addressing the spatial constraints of complex tunnel topologies. Furthermore, we introduce a “Position-Flexible” approach, where the entire panel’s location is dynamically adjusted to maximize the average sum-rate across wideband OFDM subcarriers. By exploiting frequency diversity, the proposed system effectively combats the severe frequency-selective fading inherent in multipath-rich mine tunnels. This holistic movement strategy is specifically designed to enhance hardware reliability in harsh, dust-prone mining conditions by mitigating failure risks associated with complex element-wise mechanical actuation. We formulate a joint optimization problem involving broadband active beamforming, passive phase shifts, and the STAR-RIS coordinates. To solve this non-convex problem, an Alternating Optimization (AO) algorithm is developed. Specifically, the STAR-RIS location is optimized via Projected Gradient Ascent (PGA), while the beamforming and phase-shift coefficients are refined using Successive Convex Approximation (SCA) and Semidefinite Relaxation (SDR). Simulation results confirm that the proposed system significantly improves the sum rate, validating its effectiveness for robust underground wireless connectivity.

In complex environments, cooperative agents need to convey a large amount of cognitive and decision-making information, and the huge number of parameters of neural networks increases the computational burden, and it is also very difficult to traverse all possibilities. In this paper, we enhance the architecture of the “Glue Neural Layers” (GNLs) to improve the efficiency of inter-machine communication without changing the information transfer size. We found that using ‘word’ units for communication is more effective than using ‘letters’, but too many ‘words’ can lead to a large vocabulary and affect training. Therefore, inspired by natural language, we propose to use ‘sentence’ units to reduce dictionary thickness and improve communication. Through extensive experiments, we identify the optimal balance between ‘vocabulary’ and ‘sentence length’ for different environmental complexities and verify that using ‘sentences’ is better than using ‘words’ and that there exists an optimal ‘sentence length’ for a specific task. This study sheds light on the relationship between neural architecture and language architecture.

Shared memory platforms provide a memory consistency specification (MCS) so that developers can reason about the behaviors of their parallel programs. Unfortunately, ensuring that a platform conforms to its MCS is difficult, as is exemplified by numerous bugs in well-used platforms. While existing MCS testing approaches find bugs, their efficacy depends on the testing environment (e.g. if synthetic memory pressure is applied). MCS testing environments are difficult to evaluate since legitimate MCS violations are too rare to use as an efficacy metric. As a result, prior approaches have missed critical MCS bugs.

With the increasing demand for high performance and low power consumption in embedded real-time systems, performance asymmetric multiprocessor architecture is beginning to be applied to embedded real-time systems, because this heterogeneous architecture allows the system to allocate computational resources as needed to satisfy the demands of each application and dynamic workloads. Most of the previous studies on EDZL scheduling algorithms have focused on task prioritization, and they simply assign the highest priority tasks to the fastest processors. In this paper, we choose to assign the highest priority tasks on the slowest processor and propose SSFEDZL (slowest speed fit for earliest deadline first until zero-laxity) scheduling algorithm. We compare SSF-EDZL scheduling algorithm with other scheduling algorithms and the experimental results show that SSF-EDZL is a better scheduling algorithm which has higher processor utilization and better scheduling performance.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

How to achieve reliable communication and safety monitoring is very important in coal mines. However, most of the existing transmission strategies and sensing-based monitoring approaches assume a single objective and neglect non-line-of-sight (NLOS) problems brought by winding tunnels or mine collapses. To this end, we first propose a multihop reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) approach to maximize the energy efficiency of the JCAS access point and the sum sensing rates in order to improve the sensing accuracy. Specifically, we formulate an energy-efficient optimization problem by jointly designing both the phase-shift matrix and the switches status of the RISs as well as the transmit power of the access point. The problem is solved by adopting the successive convex approximation-based alternating optimization algorithm, the second-order optimization method, the Lambert-$w$function, and Newton’s method. Moreover, a sensing-based rate optimization problem is also solved via the Lagrange relaxation method and the Gradient descent method. Simulation results demonstrate that the proposed algorithm has better robustness and higher energy efficiency.